First of all, we give an overview of the algorithm which is presented in detail in Algorithm .
[tb]
VCFR

[1] \(t<T\) 
Sample \(d_{t}\)  according to Observed rewards and transition probability from unknown environment
each update
Sample rewards from \(r \sim P(\cdot \mid z)\) , \(z\in Z\) 
Collect all \((z,r)\)  as datasets
 \((z,r)\)  in datasets
Calculate \(D_{KL}\)  for each \((z,r)\)  through the model built by BNN
Construct \(r^{\prime }=r+\eta D_{KL}\) 
Train BNN using datasets and update the distribution of environment
 \(i\in \lbrace 1,2\rbrace \)  
\(h\in H\) 
the probability that node arrives \(probs<1e-5\) 
prune this node
Calculate \(\bar{\sigma }_{i}^{t}(I)\)  by the value of regret with Regret Matching
Calculate the average strategy with information gain \(\bar{\sigma }_{i}^{T}\) .
\(i\in \lbrace 1,2\rbrace \) 
Gather the environment data and update Observed rewards and transition probability with interaction strategy \(\bar{\sigma }_{i}^{T}\) 


The posterior distribution of reward corresponding to each action is stored in the data pool \(\mathbb {L}\) . We take the prior distribution as input, and we use Bayesian Neural Network(BNN) [1]}, [2]} in VIME to obtain the posterior distribution \(\mathbb {P}\)  of the reward corresponding to each action. CFR can calculate the average strategy with the new reward added with information gain, and will explore the environment to collect the data according to the curiosity-driven strategy. The approximate Nash equilibrium will be found after continuous iterations. The whole architecture of our proposed algorithm is shown in Figure REF .
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